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Cited by 111Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareUltrasound in Clinical Applications

The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.

Application of mobile health, telemedicine and artificial intelligence to echocardiography

Open access
Seetharam, Karthik, Kagiyama, Nobuyuki, Sengupta, Partho P.·2019·Echo Research & Practice
DOI 10.1530/erp-18-0081PMC PMC6432977
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Abstract

The intersection of global broadband technology and miniaturized high-capability computing devices has led to a revolution in the delivery of healthcare and the birth of telemedicine and mobile health (mHealth). Rapid advances in handheld imaging devices with other mHealth devices such as smartphone apps and wearable devices are making great strides in the field of cardiovascular imaging like never before. Although these technologies offer a bright promise in cardiovascular imaging, it is far from straightforward. The massive data influx from telemedicine and mHealth including cardiovascular imaging supersedes the existing capabilities of current healthcare system and statistical software. Artificial intelligence with machine learning is the one and only way to navigate through this complex maze of the data influx through various approaches. Deep learning techniques are further expanding their role by image recognition and automated measurements. Artificial intelligence provides limitless opportunity to rigorously analyze data. As we move forward, the futures of mHealth, telemedicine and artificial intelligence are increasingly becoming intertwined to give rise to precision medicine.

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Registry

Crossref · 10.1530/erp-18-0081

Cites

65 of 65 references verified0 citation errors · 0 not checkable

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Figures

Figure 1 Type of handheld ultrasound machines. There are several types of handheld ultrasounds with various capabilities; a laptop-based equipment has almost every 2D echocardiographic application (panel A), while a pocket-size ultrasound does not usually have full-scale color-flow and spectral Doppler capabilities (panel B). Reproduced, with permission, from Chamsi-Pasha et al. (4).
Figure 1 Type of handheld ultrasound machines. There are several types of handheld ultrasounds with various capabilities; a laptop-based equipment has almost every 2D echocardiographic application (panel A), while a pocket-size ultrasound does not usually have full-scale color-flow and spectral Doppler capabilities (panel B). Reproduced, with permission, from Chamsi-Pasha et al. (4).CC BY-NC · PubMed Central
Figure 2 Interrogation of mHealth devices and use of artificial intelligence. Technological advancement has created a number of mobile health devices, which are available even in resource-limited areas. Involving remote experts using telemedicine helps appropriate diagnosis and management. Artificial intelligence can efficiently address the lack of experts and the influx of complex data generated by mHealth and telemedicine as well as advanced imaging modalities.
Figure 2 Interrogation of mHealth devices and use of artificial intelligence. Technological advancement has created a number of mobile health devices, which are available even in resource-limited areas. Involving remote experts using telemedicine helps appropriate diagnosis and management. Artificial intelligence can efficiently address the lack of experts and the influx of complex data generated by mHealth and telemedicine as well as advanced imaging modalities.CC BY-NC · PubMed Central
Figure 3 Growth of publications in machine learning. The x- and y-axis shows the year and the number of publications in PubMed with ‘Cardiology’ and ‘Machine Learning’. The number of publications is rapidly growing, representing huge interest in the field. Reproduced, with permission, from Shameer et al. (50).
Figure 3 Growth of publications in machine learning. The x- and y-axis shows the year and the number of publications in PubMed with ‘Cardiology’ and ‘Machine Learning’. The number of publications is rapidly growing, representing huge interest in the field. Reproduced, with permission, from Shameer et al. (50).CC BY-NC · PubMed Central
Figure 4 Association of artificial intelligence, machine learning and deep learning. Artificial intelligence (AI), though there are various definitions by itself, represents any techniques which enables computers mimic human behavior when it’s used in medical field. Machine learning is a subfield of AI, which aims at automatic discovery of regularities in data through the use of computer algorithms and generalizing those into new but similar data. Deep learning is a subset of machine learning, which makes the computation of multi-layer neural networks feasible.
Figure 4 Association of artificial intelligence, machine learning and deep learning. Artificial intelligence (AI), though there are various definitions by itself, represents any techniques which enables computers mimic human behavior when it’s used in medical field. Machine learning is a subfield of AI, which aims at automatic discovery of regularities in data through the use of computer algorithms and generalizing those into new but similar data. Deep learning is a subset of machine learning, which makes the computation of multi-layer neural networks feasible.CC BY-NC · PubMed Central

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Citations

Cites

Research works printed but not linked: 2

  1. GE Healthcare. Vscan Extend datasheet. Chicago, IL, USA: General Electric Company, 2018. (available at: https://www.gehealthcare.com/-/media/fdbbc3f456914f5dbc3cc44cb866ffb5.pdf)
  2. DeepMind. AlphaGo. London, UK: DeepMind Technologies Limited, 2019. (available at: https://deepmind.com/research/alphago/)

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Metadata

Authors
Seetharam, K., Kagiyama, N., & Sengupta, P. P.
Affiliations
West Virginia University (US)
ORCID
Venue
Echo Research & Practice
Publisher
Springer Science and Business Media LLC
Published
2019-06
Volume
6
Issue
2
Pages
R41-R52
ISSN
  • 2055-0456 (ISSN-L)
  • 2055-0464
DOI
10.1530/erp-18-0081
PMC
PMC6432977
OpenAlex
W2921183828
Cited by
111
References
65
Citations by year
2026: 7 · 2025: 10 · 2024: 18 · 2023: 14 · 2022: 23 · 2021: 22 · 2020: 13 · 2019: 4
FWCI
0.76
Type
journal article
Language
en
Open access
Licence
Locations
  • Echo Research and Practice (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • The Research Repository @ WVU (West Virginia University) (submittedVersion, cc-by-nc)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Ultrasound in Clinical Applications
Keywords
mobile health, telemedicine, artificial intelligence, echocardiography, cardiovascular imaging, handheld ultrasound, mobile application, wearable devices, deep learning, automated image analysis, machine learning, precision medicine

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